Deep learning (DL) is becoming popular as a new tool for many applications in
wireless communication systems. However, for many classification tasks (e.g.,
modulation classification) it has been shown that DL-based wireless systems are
susceptible to adversarial examples; adversarial examples are well-crafted
malicious inputs to the neural network (NN) with the objective to cause
erroneous outputs. In this paper, we extend this to regression problems and
show that adversarial attacks can break DL-based power allocation in the
downlink of a massive multiple-input-multiple-output (maMIMO) network.
Specifically, we extend the fast gradient sign method (FGSM), momentum
iterative FGSM, and projected gradient descent adversarial attacks in the
context of power allocation in a maMIMO system. We benchmark the performance of
these attacks and show that with a small perturbation in the input of the NN,
the white-box attacks can result in infeasible solutions up to 86%.
Furthermore, we investigate the performance of black-box attacks. All the
evaluations conducted in this work are based on an open dataset and NN models,
which are publicly available.

本文研究了深度学习在无线通信系统中应用的安全性问题，使用快速梯度符号法、动量迭代快速梯度符号法和方向性梯度下降法攻击了大规模多输入多输出系统中功率分配的神经网络，发现在神经网络输入的微小扰动下能使白盒攻击的收益率达到 86%。